自相似业务流量建模与性能评价研究
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摘要
大量实际测量发现网络流量具有普遍的自相似性(或长相关性),对网络业务建模、性能评价和网络控制技术产生了重要影响。传统的网络模型在描述实际网络业务时,认为网络流量具有Markov性,并在此基础上建立了以Poisson过程为主的数学描述模型,这种模型具有短程相关结构。长程相关性在多个时间尺度上存在,并且在大时间尺度上对网络时延、抖动、丢包率以及吞吐量等网络性能具有重大的影响。自相似流量建模及性能分析已成为当前研究的热点。
本文深入研究自相似流量建模和性能评价问题。论文首先介绍自相似的常见定义,描述自相似过程在数学和物理上的若干特征;研究网络自相似业务的建模与流量数据生成方法,并对这些业务模型的性能进行了分析;通过仿真实验研究了自相似特性对网络性能的影响。
在网络流量建模和性能分析方面,目前提出的流量模型较多,本文集中精力重点研究了基于FBM和FARIMA的流量建模和网络性能评价。首先通过数学建模,推导出基于模型的理论分析结果,然后通过OPNET仿真来验证理论分析结果,以测试现有自相似业务模型的精确度及其适用范围,找出适合于刻画各种不同应用特性的自相似业务模型。
文中利用能够反映自相似特性的FBM模型,采用G/D/1排队模型研究了自相似性对网络性能的影响,讨论了在Norros给出的缓冲区溢出概率公式的基础上,FBM模型为输入时,网络性能指标的解析公式。通过理论分析与仿真相结合的方法研究了包丢失概率、平均时延、队列长度等性能指标随Hurst系数、缓冲区长度、利用率、方差和负载等模型参数的变化情况,发现除了Hurst系数外,缓冲区长度、利用率、方差和负载等参数对系统的性能也存在重要的影响,有的影响甚至比Hurst系数还要大,传统的只考虑Hurst系数的性能评价方法既不全面,有时还可能会发生误导。研究结果还发现,FBM模型性能具有明显的时间尺度特性,长短时间尺度的性能支配因素不同,它们之间存在状态转变或突变。
实际网络测量还显示,网络业务同时呈现长相关和短相关特性,长短相关对网络的性能产生了极大的影响,因此建立可以能够同时描述长相关和短相关特性的网络业务模型是个重要的问题。文中给出了利用FARIMA模型进行建模、拟合实际网络流量的方法和参数估计的具体步骤,研究了长短相关对网络性能的影响。研究表明:FARIMA模型可以较精确地拟合实际业务的长相关和短相关;当缓存较小时,网络性能将由短相关特性支配,而且随着缓冲区增加时,长相关业务下系统的衰减要比短相关模型下的衰减方式慢,这些发现对今后网络设计性能研究具有重要的参考价值。
The discovery of the self-similar characteristic of network traffic has great influence on network traffic modeling, performance evaluation and network control. Traditional Poisson-based models of network traffic are based on the hypothesis of Markov which has the nature of short-range dependence (SRD). Recent traffic analysis from various packet networks shows that network traffic processes exhibit ubiquitous properties of self-similarity and long range dependence (LRD), i.e. of correlation over a wide range of time scales. LRD exists on multiple time scales and has great influences on network performances such as delay, jitter, cell loss rate and throughput on the large time scale. Modeling and performance analysis of self-similar traffic becomes Current hotspot.
In this thesis, the problems of network modeling and performance evaluation of self-similar traffic are studied with depth. Firstly, several mathematical definitions of self-similarity are given. Some mathematical and physical features describing the self-similar processes are described. The methods of modeling and generation of the self-similar traffic are discussed. The performance of these models is analyzed. The influence of network performance on self-similariy is studied through simulation.
Though many traffic models have been proprosed, this thesis focus on the problems of FBM and FARIMA -based traffic modeling and performance analysis. Mathematical model is provided, and its theory results are derived.In addition, the validity and efficiency of these results are conformed through simulation based on OPNET, so as to test the precision and adaptability of existing self-similar model and find out the best traffic model for givven applications.
The FBM traffic model which holds self-similarity is used to study the performance of the G/D/1 queuing model. Based on the buffer overflow rate given by Norros, the asymptotic analytic expression of the network performance with FBM input is obtained. The variance of the system performance indices, such as packet loss probability, effective bandwidth, average delay and queue length, with the model parameters, namely Hurst index, buffer size, utilization ,variance and load of the traffic, is studied through theoretical analysis and simulation. The results show that beside Hurst index, several other parameters, such as buffer size, utilization ,variance and load of the traffic have also great influence on system performance, some influence are even greater than that of Hurst index. The traditional concept which only consist the influence of Hurst index is not overall, it may be sometimes misleading. The study results also reveal that the performance of the FBM model has obviously the characteristic of time scale, the dominative factors of large and small time scale are deffernent, and there is a state change or abrupt changes between small time scale and large time scale.
Measurement also shows that network traffic exhibits properties of short-range and long-range dependence. Short-range and long-range dependence have great impact on network performance. FARIMA (p, d, q) model is a good traffic model capable of capturing both long-range and short-range behavior of network traffic. In this paper, FARIMA(p,d,q) model is used to model, generate traffic and estimate parameters which fit the actual traffic trace. The results of analysis and simulation demonstrate that FARIMA model fits real multimedia traffic very good. The short-range dependence is the ascendant with small buffer, while network performance reduces lower in long-range traffic than short-range traffic. These results are very important for the future research of the network performance.
本文深入研究自相似流量建模和性能评价问题。论文首先介绍自相似的常见定义,描述自相似过程在数学和物理上的若干特征;研究网络自相似业务的建模与流量数据生成方法,并对这些业务模型的性能进行了分析;通过仿真实验研究了自相似特性对网络性能的影响。
在网络流量建模和性能分析方面,目前提出的流量模型较多,本文集中精力重点研究了基于FBM和FARIMA的流量建模和网络性能评价。首先通过数学建模,推导出基于模型的理论分析结果,然后通过OPNET仿真来验证理论分析结果,以测试现有自相似业务模型的精确度及其适用范围,找出适合于刻画各种不同应用特性的自相似业务模型。
文中利用能够反映自相似特性的FBM模型,采用G/D/1排队模型研究了自相似性对网络性能的影响,讨论了在Norros给出的缓冲区溢出概率公式的基础上,FBM模型为输入时,网络性能指标的解析公式。通过理论分析与仿真相结合的方法研究了包丢失概率、平均时延、队列长度等性能指标随Hurst系数、缓冲区长度、利用率、方差和负载等模型参数的变化情况,发现除了Hurst系数外,缓冲区长度、利用率、方差和负载等参数对系统的性能也存在重要的影响,有的影响甚至比Hurst系数还要大,传统的只考虑Hurst系数的性能评价方法既不全面,有时还可能会发生误导。研究结果还发现,FBM模型性能具有明显的时间尺度特性,长短时间尺度的性能支配因素不同,它们之间存在状态转变或突变。
实际网络测量还显示,网络业务同时呈现长相关和短相关特性,长短相关对网络的性能产生了极大的影响,因此建立可以能够同时描述长相关和短相关特性的网络业务模型是个重要的问题。文中给出了利用FARIMA模型进行建模、拟合实际网络流量的方法和参数估计的具体步骤,研究了长短相关对网络性能的影响。研究表明:FARIMA模型可以较精确地拟合实际业务的长相关和短相关;当缓存较小时,网络性能将由短相关特性支配,而且随着缓冲区增加时,长相关业务下系统的衰减要比短相关模型下的衰减方式慢,这些发现对今后网络设计性能研究具有重要的参考价值。
The discovery of the self-similar characteristic of network traffic has great influence on network traffic modeling, performance evaluation and network control. Traditional Poisson-based models of network traffic are based on the hypothesis of Markov which has the nature of short-range dependence (SRD). Recent traffic analysis from various packet networks shows that network traffic processes exhibit ubiquitous properties of self-similarity and long range dependence (LRD), i.e. of correlation over a wide range of time scales. LRD exists on multiple time scales and has great influences on network performances such as delay, jitter, cell loss rate and throughput on the large time scale. Modeling and performance analysis of self-similar traffic becomes Current hotspot.
In this thesis, the problems of network modeling and performance evaluation of self-similar traffic are studied with depth. Firstly, several mathematical definitions of self-similarity are given. Some mathematical and physical features describing the self-similar processes are described. The methods of modeling and generation of the self-similar traffic are discussed. The performance of these models is analyzed. The influence of network performance on self-similariy is studied through simulation.
Though many traffic models have been proprosed, this thesis focus on the problems of FBM and FARIMA -based traffic modeling and performance analysis. Mathematical model is provided, and its theory results are derived.In addition, the validity and efficiency of these results are conformed through simulation based on OPNET, so as to test the precision and adaptability of existing self-similar model and find out the best traffic model for givven applications.
The FBM traffic model which holds self-similarity is used to study the performance of the G/D/1 queuing model. Based on the buffer overflow rate given by Norros, the asymptotic analytic expression of the network performance with FBM input is obtained. The variance of the system performance indices, such as packet loss probability, effective bandwidth, average delay and queue length, with the model parameters, namely Hurst index, buffer size, utilization ,variance and load of the traffic, is studied through theoretical analysis and simulation. The results show that beside Hurst index, several other parameters, such as buffer size, utilization ,variance and load of the traffic have also great influence on system performance, some influence are even greater than that of Hurst index. The traditional concept which only consist the influence of Hurst index is not overall, it may be sometimes misleading. The study results also reveal that the performance of the FBM model has obviously the characteristic of time scale, the dominative factors of large and small time scale are deffernent, and there is a state change or abrupt changes between small time scale and large time scale.
Measurement also shows that network traffic exhibits properties of short-range and long-range dependence. Short-range and long-range dependence have great impact on network performance. FARIMA (p, d, q) model is a good traffic model capable of capturing both long-range and short-range behavior of network traffic. In this paper, FARIMA(p,d,q) model is used to model, generate traffic and estimate parameters which fit the actual traffic trace. The results of analysis and simulation demonstrate that FARIMA model fits real multimedia traffic very good. The short-range dependence is the ascendant with small buffer, while network performance reduces lower in long-range traffic than short-range traffic. These results are very important for the future research of the network performance.
引文
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